https://github.com/abhishekyana/cyclegans-pytorch

CycleGANs-PyTorch applied on Young to Old image converter.
https://github.com/abhishekyana/cyclegans-pytorch

cuda cyclegan faceapp gan python pytorch resnet tutorial-code young2old

Last synced: 3 months ago
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CycleGANs-PyTorch applied on Young to Old image converter.

• Host: GitHub
• URL: https://github.com/abhishekyana/cyclegans-pytorch
• Owner: abhishekyana
• License: mit
• Created: 2019-07-24T15:12:37.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-22T04:08:53.000Z (about 2 years ago)
• Last Synced: 2023-06-02T01:00:46.841Z (over 1 year ago)
• Topics: cuda, cyclegan, faceapp, gan, python, pytorch, resnet, tutorial-code, young2old
• Language: Python
• Homepage:
• Size: 3.91 MB
• Stars: 15
• Watchers: 2
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CycleGANs-PyTorch applied on Young to Old image converter.

Py-Torch implementation of [CycleGANs Paper](https://arxiv.org/pdf/1703.10593.pdf).

* You can find more about this project in my [blog here](http://blog.abhishekyana.ml/implement-your-own-young-to-old-age-converter-app-in-pytorch-using-cyclegans/).

### CycleGAN Block Diagram:

![BD](./CycleGANBD.png)

### RESULTS FIRST: Young to Old converter

![img1.jpg](./results/A/0166.png)

![img2.jpg](./results/A/0168.png)

![img3.jpg](./results/A/0145.png)

![img4.jpg](./results/A/0255.png)

### IF you want to replicate these results may be on different dataset. Read More..

1. Clone the repository:

```

git clone https://github.com/abhishekyana/CycleGANs-PyTorch.git

cd CycleGANs-PyTorch

# As this is a huge project, I'd suggest to make a conda environment and then run the training and all.

```

1. Install all the requirements from requirements.txt file:

1. Download the dataset, It can be grabbed from [here](https://www.kaggle.com/abhishekyana/young2old-dataset).

1. Unzip and Move the dataset folder into this project's root directory.

1. Adjust the configure.py file according to your flavour, these parameters affect the training.

1. Run the `python train.py` file and see the training happen for yourself.

* The models will be saved to and loaded from ./outputs as default.

* The model trained for around 4 hours on GTX1080 and i7 system.

### If you want to test the mode, then you can download the pretrained model [from here](./). Sorry the link is broken I'll fix it..

* Download the dataset.

* Download the pretrained model. Only Generator model is enough.

* Copy these folders into appropriate directories as mentioned above.

* Run `python test.py`, After the provess is done, you can see the Juxtaposed results in `./outputs/A` and `./outputs/B`.

* If you want to run this on your own images, Copy your image into a directory in `./directory/A` if you want to make your picture old or into `./directory/B` if you want your picture to be Young. Then edit the `./directory` in testoptions in `configure.py` and run the code again. Now, you can see the your image in the outputs directory.

### Please Feel Free to Fork it, Clone it and whatever you want.

* Not only this data, A CycleGAN can map from any unpaired domains, as this application si trending now, I've chosen this to code. 

## With Love on Open Source

### Thank you

This project is inspired from [Aitor Ruano](https://github.com/aitorzip) and I would like to thank him for providing such a beautiful code which I used to clarify my doubts during the implementation.